Scaling Data Science at Airbnb

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Delivered by Elena Grewal at the 2016 New York R Conference on April 8th and 9th at Work-Bench.

Published in: Data & Analytics

Scaling Data Science at Airbnb

  1. 1. Scaling Data Science Structure + tooling at Airbnb Elena Grewal / April 9, 2016 / @elenatej
  2. 2. I started at Airbnb ~4 years ago ● Ph.D. in Education ● Team grown from 5 to 70 ○ Fun fact for this conference: ■ 54% mostly use R, 29% mostly use Python ■ 80% very comfortable with R, 50% with Python ● Company grown from 200 to 2,500
  3. 3. Scaling ● Team growth ● Interview process ● Why diversity ● Using data ○ Top of funnel changes ○ Conversion changes ● Scaling diversity data science
  4. 4. Rapid Data Team Growth
  5. 5. Diversity was important to us, but it wasn’t happening Women :( Team Size
  6. 6. Interview process focused on practical data skills ‘Data challenges’ - Airbnb data + real question Multi-stage - Recruiter screen - Take home data challenge - Onsite challenge - 1:1s with hiring manager, business partner, CV
  7. 7. We felt good about the data challenges and process ● Popular Quora post ● Process starts being used by other companies (!)
  8. 8. Why act now ● Harder to hire women as ratio declines ● Women could feel excluded on team ● Homogeneity -> narrower range of ideas
  9. 9. We believe in a world where people belong, anywhere.
  10. 10. We started by looking at the data. -
  11. 11. ● Manual audit of past apps ● EEOC data on inbound applicants
  12. 12. FUNNEL IMAGE 30% women No drop off Drop off Drop off
  13. 13. We then thought about everything we could possibly do to make a difference And we did those things.
  14. 14. FUNNEL IMAGE
  15. 15. Lightning talks Support community Diversity on multiple dimensions Encourage applicants
  16. 16. Blog Posts & Interviews Highlight Women @ Airbnb Inspire women in data more broadly
  17. 17. Women in data dinners Create community of senior women in field Circulates to multiple companies (not just Airbnb)
  18. 18. ● Create standard rubric ● Binary scoring system ● Removed names for a bit ● Trained graders ● Two graders for each test to ensure consistency
  19. 19. ● “Buddy” coffee chat & support ● 50% women at presentation ● Clearer success criteria
  20. 20. Increase in Female Hires High employee satisfaction scores + 100% women belong
  21. 21. Our work is not complete.
  22. 22. Next steps ● Focus on multiple aspects of diversity ○ Apply similar process to thinking about racial diversity ○ Other dimensions as well ○ Continue to improve interview process for all - stay vigilant ● Continue to monitor team culture and belonging of current employees ● Help the rest of the company and scale the efforts
  23. 23. Scaling our efforts What about the rest of Airbnb? ● Full time data scientist + data engineer to work with our “People and culture team” ● AWS account held separate from main Airbnb data ● Built tool to request and collect diversity data from referrals and passively sourced candidates ● Dashboards with diversity data for every team
  24. 24. Thank you!

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